code: def __init__(
      self,
      mu=0.0,
      sigma=1.0,
      sigma_min=None,
      n_steps_annealing=1000,
      size=1,
  ):
      super(GaussianWhiteNoiseProcess, self).__init__(
          mu=mu,
          sigma=sigma,
          sigma_min=sigma_min,
          n_steps_annealing=n_steps_annealing,
      )
      self.size = size
  
file_path: keras-rl/rl/random.py
imports: from __future__ import division
  import numpy
label_desc: Use image node_id 1 to create a new GaussianWhiteNoiseProcess object from inherited base classes: AnnealedGaussianProcess with example: obj = GaussianWhiteNoiseProcess(mu, sigma, sigma_min, n_steps_annealing, size)